A procedure can be assisted by a processor system, such as a computer system. A trajectory can be used to identify a selected trajectory or path of an instrument to reach a tumor within a brain of a subject, reach a selected portion of the anatomy (e.g. sub-thalamic nucleus (STN) or spinal cord), or other appropriate target. The planning algorithm can include both inputted data and learned rankings or ratings related to selected trajectories. The planning algorithm can used the learned ratings to rate and later determined trajectories.

A procedure can be assisted by a processor system, such as a computer system. A trajectory can be used to identify a selected trajectory or path of an instrument to reach a tumor within a brain of a subject, reach a selected portion of the anatomy (e.g. sub-thalamic nucleus (STN) or spinal cord), or other appropriate target. The planning algorithm can include both inputted data and learned rankings or ratings related to selected trajectories. The planning algorithm can used the learned ratings to rate and later determined trajectories.

This disclosure includes techniques for analyzing patient data. In one example, a method includes accessing, by a computer system, one or more databases comprising health information, with protected health information, for a plurality of patients, accessing, by the computer system, an analytical model, and receiving, by the computer system via a user interface, instructions to apply the analytical model to health information for each of the plurality of patients. The protected health information is isolated from the user interface to restrict access to the protected health information. The method further includes applying, by the computer system, the analytical model to health information for each of the plurality of patients, and storing a result of the analytical model to the one or more databases.

This disclosure includes techniques for analyzing patient data. In one example, a method includes accessing, by a computer system, one or more databases comprising health information, with protected health information, for a plurality of patients, accessing, by the computer system, an analytical model, and receiving, by the computer system via a user interface, instructions to apply the analytical model to health information for each of the plurality of patients. The protected health information is isolated from the user interface to restrict access to the protected health information. The method further includes applying, by the computer system, the analytical model to health information for each of the plurality of patients, and storing a result of the analytical model to the one or more databases.

According to one aspect of the invention, health insurance claim data for a first group of individuals is obtained to generate a training corpus, including a training set of claim data and a holdout set of claim data. The first group of individuals represents enrollees of one or more first health insurance plans and the health insurance claim data represents historic insurance claim information for each individual in the first group. A Bayesian belief network (BBN) model is created by training a BBN network based on the training set of claim data using predetermined machine learning algorithms. The BBN model is validated using the holdout set of claim data. The BBN model, when having been successfully validated, is configured to identify at least one of individuals with risk for a disorder and individuals with risk who are most likely to benefit from intervention and treatment for the disorder.

According to one aspect of the invention, health insurance claim data for a first group of individuals is obtained to generate a training corpus, including a training set of claim data and a holdout set of claim data. The first group of individuals represents enrollees of one or more first health insurance plans and the health insurance claim data represents historic insurance claim information for each individual in the first group. A Bayesian belief network (BBN) model is created by training a BBN network based on the training set of claim data using predetermined machine learning algorithms. The BBN model is validated using the holdout set of claim data. The BBN model, when having been successfully validated, is configured to identify at least one of individuals with risk for a disorder and individuals with risk who are most likely to benefit from intervention and treatment for the disorder.

The present invention relates generally to systems and methods for measuring an analyte in a host. More particularly, the present invention relates to systems and methods for processing sensor data, including calculating a rate of change of sensor data and/or determining an acceptability of sensor or reference data.

The present invention relates generally to systems and methods for measuring an analyte in a host. More particularly, the present invention relates to systems and methods for processing sensor data, including calculating a rate of change of sensor data and/or determining an acceptability of sensor or reference data.

In examples, a method of controlling customer access to an assay system comprises (a) receiving a system identifier; (b) identifying said system identifier; and (c) utilizing information obtained from the system identifier to perform one or more operations selected from: (i) enabling full access to said system and/or a consumable used in said system; (ii) enabling partial access to said system and/or a consumable used in said system; or (iii) denying access to said system and/or a consumable used in said system.

In examples, a method of controlling customer access to an assay system comprises (a) receiving a system identifier; (b) identifying said system identifier; and (c) utilizing information obtained from the system identifier to perform one or more operations selected from: (i) enabling full access to said system and/or a consumable used in said system; (ii) enabling partial access to said system and/or a consumable used in said system; or (iii) denying access to said system and/or a consumable used in said system.